





import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class   a_ (			_A		,							unittest.TestCase      ):
			A__      :			int       	=  KandinskyVaaControlnetPipeline
			A__      :			List[Any]       	=  ['image_embeds', 'negative_image_embeds', 'hint']
			A__      :			int       	=  ['image_embeds', 'negative_image_embeds', 'hint']
			A__      :			Dict       	=  [
			    'generator',
			    'height',
			    'width',
			    'latents',
			    'guidance_scale',
			    'num_inference_steps',
			    'return_dict',
			    'guidance_scale',
			    'num_images_per_prompt',
			    'output_type',
			    'return_dict',
			]
			A__      :			Optional[Any]       	=  False

			@property
			def      lowerCAmelCase(   self   :  Tuple						):






							"""simple docstring"""
							return 32

			@property
			def      lowerCAmelCase(   self   :  Any						):






							"""simple docstring"""
							return 32

			@property
			def      lowerCAmelCase(   self   :  str						):






							"""simple docstring"""
							return self.time_input_dim

			@property
			def      lowerCAmelCase(   self   :  Optional[Any]						):






							"""simple docstring"""
							return self.time_input_dim * 4

			@property
			def      lowerCAmelCase(   self   :  Optional[int]						):






							"""simple docstring"""
							return 100

			@property
			def      lowerCAmelCase(   self   :  Any						):






							"""simple docstring"""
							torch.manual_seed(0						)

							snake_case   :					str    						=				{
							    "in_channels": 8,
							    # Out channels is double in channels because predicts mean and variance
							    "out_channels": 8,
							    "addition_embed_type": "image_hint",
							    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
							    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
							    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
							    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
							    "layers_per_block": 1,
							    "encoder_hid_dim": self.text_embedder_hidden_size,
							    "encoder_hid_dim_type": "image_proj",
							    "cross_attention_dim": self.cross_attention_dim,
							    "attention_head_dim": 4,
							    "resnet_time_scale_shift": "scale_shift",
							    "class_embed_type": None,
							}

							snake_case   :					Union[str, Any]    						=				UNetaDConditionModel(**a_						)
							return model

			@property
			def      lowerCAmelCase(   self   :  Tuple						):






							"""simple docstring"""
							return {
							    "block_out_channels": [32, 32, 64, 64],
							    "down_block_types": [
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							        "AttnDownEncoderBlock2D",
							    ],
							    "in_channels": 3,
							    "latent_channels": 4,
							    "layers_per_block": 1,
							    "norm_num_groups": 8,
							    "norm_type": "spatial",
							    "num_vq_embeddings": 12,
							    "out_channels": 3,
							    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
							    "vq_embed_dim": 4,
							}

			@property
			def      lowerCAmelCase(   self   :  Dict						):






							"""simple docstring"""
							torch.manual_seed(0						)
							snake_case   :					Tuple    						=				VQModel(**self.dummy_movq_kwargs						)
							return model

			def      lowerCAmelCase(   self   :  Optional[Any]						):






							"""simple docstring"""
							snake_case   :					Tuple    						=				self.dummy_unet
							snake_case   :					Optional[int]    						=				self.dummy_movq

							snake_case   :					Any    						=				DDIMScheduler(
							    num_train_timesteps=1_000     , beta_schedule='''linear'''     , beta_start=0.0_0085     , beta_end=0.012     , clip_sample=a_     , set_alpha_to_one=a_     , steps_offset=1     , prediction_type='''epsilon'''     , thresholding=a_     , )

							snake_case   :					Union[str, Any]    						=				{
							    "unet": unet,
							    "scheduler": scheduler,
							    "movq": movq,
							}
							return components

			def      lowerCAmelCase(   self   :  Tuple     , UpperCAmelCase__   :  Any     , UpperCAmelCase__   :  List[str]=0						):






							"""simple docstring"""
							snake_case   :					Optional[int]    						=				floats_tensor((1, self.text_embedder_hidden_size)     , rng=random.Random(a_						)						).to(a_						)
							snake_case   :					Any    						=				floats_tensor((1, self.text_embedder_hidden_size)     , rng=random.Random(seed + 1						)						).to(
							    a_						)

							# create hint
							snake_case   :					int    						=				floats_tensor((1, 3, 64, 64)     , rng=random.Random(a_						)						).to(a_						)

							if str(a_						).startswith('''mps'''						):
											snake_case   :					Optional[int]    						=				torch.manual_seed(a_						)
							else:
											snake_case   :					Optional[Any]    						=				torch.Generator(device=a_						).manual_seed(a_						)
							snake_case   :					Dict    						=				{
							    "image_embeds": image_embeds,
							    "negative_image_embeds": negative_image_embeds,
							    "hint": hint,
							    "generator": generator,
							    "height": 64,
							    "width": 64,
							    "guidance_scale": 4.0,
							    "num_inference_steps": 2,
							    "output_type": "np",
							}
							return inputs



			def      lowerCAmelCase(   self   :  Any						):






							"""simple docstring"""
							snake_case   :					Any    						=				"cpu"

							snake_case   :					int    						=				self.get_dummy_components()

							snake_case   :					Any    						=				self.pipeline_class(**a_						)
							snake_case   :					Dict    						=				pipe.to(a_						)

							pipe.set_progress_bar_config(disable=a_						)

							snake_case   :					Optional[Any]    						=				pipe(**self.get_dummy_inputs(a_						)						)
							snake_case   :					int    						=				output.images

							snake_case   :					Any    						=				pipe(
							    **self.get_dummy_inputs(a_						)     , return_dict=a_     , )[0]

							snake_case   :					List[Any]    						=				image[0, -3:, -3:, -1]
							snake_case   :					Dict    						=				image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)

							snake_case   :					Optional[Any]    						=				np.array(
							    [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]						)

							assert (
							    np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
							), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"

							assert (
							    np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2
							), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
@slow
@require_torch_gpu
class   a_ (			unittest.TestCase      ):

			def      lowerCAmelCase(   self   :  Tuple						):






							"""simple docstring"""
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



			def      lowerCAmelCase(   self   :  Dict						):






							"""simple docstring"""
							snake_case   :					Optional[int]    						=				load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'''						)

							snake_case   :					int    						=				load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/kandinskyv22/hint_image_cat.png'''						)
							snake_case   :					Optional[Any]    						=				torch.from_numpy(np.array(a_						)						).float() / 255.0
							snake_case   :					Union[str, Any]    						=				hint.permute(2     , 0     , 1						).unsqueeze(0						)

							snake_case   :					Any    						=				KandinskyVaaPriorPipeline.from_pretrained(
							    '''kandinsky-community/kandinsky-2-2-prior'''     , torch_dtype=torch.floataa						)
							pipe_prior.to(a_						)

							snake_case   :					str    						=				KandinskyVaaControlnetPipeline.from_pretrained(
							    '''kandinsky-community/kandinsky-2-2-controlnet-depth'''     , torch_dtype=torch.floataa						)
							snake_case   :					Dict    						=				pipeline.to(a_						)
							pipeline.set_progress_bar_config(disable=a_						)

							snake_case   :					Optional[int]    						=				"A robot, 4k photo"

							snake_case   :					Optional[int]    						=				torch.Generator(device='''cuda'''						).manual_seed(0						)
							snake_case   :					Optional[int]    						=				pipe_prior(
							    a_     , generator=a_     , num_inference_steps=5     , negative_prompt=''''''     , ).to_tuple()

							snake_case   :					List[str]    						=				torch.Generator(device='''cuda'''						).manual_seed(0						)
							snake_case   :					int    						=				pipeline(
							    image_embeds=a_     , negative_image_embeds=a_     , hint=a_     , generator=a_     , num_inference_steps=100     , output_type='''np'''     , )

							snake_case   :					Any    						=				output.images[0]

							assert image.shape == (512, 512, 3)

							assert_mean_pixel_difference(a_     , a_						)







'''simple docstring'''






from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst






def   __A    (				):
		lowerCAmelCase						,		lowerCAmelCase  :     List[Any]       						= 9, 1_4  # noqa: F841
		lowerCAmelCase  :     int       						= [
		    [0, 1, 4],
		    [0, 7, 8],
		    [1, 2, 8],
		    [7, 8, 7],
		    [7, 6, 1],
		    [2, 8, 2],
		    [8, 6, 6],
		    [2, 3, 7],
		    [2, 5, 4],
		    [6, 5, 2],
		    [3, 5, 1_4],
		    [3, 4, 9],
		    [5, 4, 1_0],
		    [1, 7, 1_1],
		]

		lowerCAmelCase  :     Optional[Any]       						= defaultdict(a_						)
		for nodea, nodea, cost in edges:
				adjancency[nodea].append([nodea, cost]						)
				adjancency[nodea].append([nodea, cost]						)

		lowerCAmelCase  :     int       						= mst(a_						)

		lowerCAmelCase  :     Tuple       						= [
		    [7, 6, 1],
		    [2, 8, 2],
		    [6, 5, 2],
		    [0, 1, 4],
		    [2, 5, 4],
		    [2, 3, 7],
		    [0, 7, 8],
		    [3, 4, 9],
		]

		for answer in expected:
				lowerCAmelCase  :     List[str]       						= tuple(answer[:2]						)
				lowerCAmelCase  :     int       						= tuple(edge[::-1]						)
				assert edge in result or reverse in result



